Instructblip Flan T5 Xxl

🚀 InstructBLIP model

The InstructBLIP model uses Flan - T5 - xxl as its language model. It was introduced in the paper InstructBLIP: Towards General - purpose Vision - Language Models with Instruction Tuning by Dai et al.

Disclaimer: The team releasing InstructBLIP did not write a model card for this model, so this model card has been written by the Hugging Face team.

🚀 Quick Start

The InstructBLIP model can be used for image - text - to - text tasks. Here is a basic example of how to use it:

from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration
import torch
from PIL import Image
import requests

model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-flan-t5-xxl")
processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-flan-t5-xxl")

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
prompt = "What is unusual about this image?"
inputs = processor(images=image, text=prompt, return_tensors="pt").to(device)

outputs = model.generate(
        **inputs,
        do_sample=False,
        num_beams=5,
        max_length=256,
        min_length=1,
        top_p=0.9,
        repetition_penalty=1.5,
        length_penalty=1.0,
        temperature=1,
)
generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()
print(generated_text)

✨ Features

• Visual Instruction Tuned: InstructBLIP is a visual instruction tuned version of BLIP - 2.

📚 Documentation

Model description

InstructBLIP is a visual instruction tuned version of BLIP - 2. Refer to the paper for details.

Intended uses & limitations

Usage is as described in the code example above.

How to use

For more code examples, refer to the documentation.

Ethical Considerations

This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high - risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.

📄 License

This project is licensed under the MIT license.

⚠️ Important Note

The team releasing InstructBLIP did not write a model card for this model, so this model card has been written by the Hugging Face team.

💡 Usage Tip

For more code examples, refer to the documentation.